www n http://www.cs.berkeley.edu/~pabbeel/cs287-fa11 n [Step through - PDF document

CS 287: Advanced Robotics Fall 2011 Lecture 1: Introduction Pieter Abbeel UC Berkeley EECS www n http://www.cs.berkeley.edu/~pabbeel/cs287-fa11 n [Step through webpage] Page 1 �

Remainder of Lecture Outline n Questions? n A few robotic success stories Driverless Cars Darpa Grand Challenge: First long-distance driverless car competition n 2004: CMU vehicle drove 7.36 out of 150 miles n 2005: 5 teams finished, Stanford team won nova-race n Darpa Urban Challenge (2007) n Urban environment: other vehicles present n 6 teams finished (CMU won) urban challenge n Google Autonomous Cars (2010) n Mountain View -> Santa Monica; >140,000 miles; Lombard, Golden Gate, Tahoe, Pacific n Coast Highway Ernst Dickmanns / Mercedes Benz: autonomous car on European highways n Human in car for interventions n Paris highway and 1758km trip Munich -> Odense, lane changes at up to 140km/h; longest n autonomous stretch: 158km (1995) Maneuvers: parking, n Kalman filtering, LQR, mapping, terrain & object recognition Page 2 �

Autonomous Helicopter Flight [Coates, Abbeel & Ng] Kalman filtering, model-predictive control, LQR, system ID, trajectory learning Four-legged locomotion [Kolter, Abbeel & Ng] value iteration, receding horizon control, motion planning, inverse reinforcement learning, nolearning, learned Page 3 �

Two-legged locomotion [Tedrake +al.] Policy gradient Mapping [Video from W. Burgard and D. Haehnel] “baseline” : Raw odometry data + laser range finder scans Page 4 �

Mapping [Video from W. Burgard and D. Haehnel] FastSLAM: particle filter + occupancy grid mapping Mobile Manipulation [Quigley, Gould, Saxena, Ng + al.] SLAM, localization, motion planning for navigation and grasping, grasp point selection, visual category recognition (speech recognition and synthesis) Page 5 �

Mobile Manipulation [Maitin-Shepard, Cusumano-Towner, Lei, Abbeel, 2010] localization, motion planning for navigation and grasping, grasp point selection, visual recognition Why a Great Time to Study CS287 Advanced Robotics? n Robotic hardware is getting in great shape, expertise in algorithms+math+programming are limiting factors n So many different robotic systems, yet a few core techniques, which I believe can be learned through the course of this semester, are (near-)sufficient to rule them all n Probabilistic Reasoning n Optimization n Applicability of these techniques extends well beyond robotics Page 6 �

That’s it for today n Starting probabilistic reasoning on Tuesday n Check out the webpage! n Sign up on piazza! n Come talk to me now about any lingering questions you might have Page 7 �